Within the domain of data mining, one critical objective is the discovery of sequential rules with high utility. The goal is to discover sequential rules that exhibit both high utility and strong confidence, which are valuable in real-world applications. However, existing high-utility sequential rule mining algorithms suffer from redundant utility computations, as different rules may consist of the same sequence of items. When these items can form multiple distinct rules, additional utility calculations are required. To address this issue, this study proposes a sequential rule mining algorithm that utilizes segmentation guided by confidence (RSC), which employs confidence-guided segmentation to reduce redundant utility computation. It adopts a method that precomputes the confidence of segmented rules by leveraging the support of candidate subsequences in advance. Once the segmentation point is determined, all rules with different antecedents and consequents are generated simultaneously. RSC uses a utility-linked table to accelerate candidate sequence generation and introduces a stricter utility upper bound, called the reduced remaining utility of a sequence, to address sequences with duplicate items. Finally, the proposed RSC method was evaluated on multiple datasets, and the results demonstrate improvements over state-of-the-art approaches.

High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this article, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU , which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O (1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low-utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this article, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely, discovering high-utility sequential patterns with the seqPro structure (HUSP-SP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns’ utilities and upper-bound values. Furthermore, a new upper bound on utility, namely, tighter reduced sequence utility and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.

Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds (suffix remain utility and terminated descendants utility) as well as a vertical last instance table. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this article, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS + , to extract on-shelf high-utility sequential patterns. For further efficiency, we also design several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility ( TPEU ) and time reduced sequence utility ( TRSU ). In addition, two novel data structures are developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS + has wider real-life applications owing to its high efficiency.

Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability and interpretability. To obtain a human-explainable data intelligence model for decision making, in this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. Meanwhile, a more normative formulation of the problem of fuzzy utility mining on sequences is formulated. By exploring fuzzy set theory for utility mining, we propose a novel method termed pattern growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, we develop two compressed data structures with explainable fuzziness. Furthermore, one existing and two new upper bounds on the explainable fuzzy utility of candidates are adopted in three proposed pruning strategies to substantially reduce the search space and, thus, expedite the mining process. It is demonstrated that PGFUM achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost.

Anomaly detection for log sequences is a necessary task for system intelligent operation and fault diagnosis. In a log sequence, adjacent logs have the property of local correlation, while long-distance logs have remote dependencies. It is helpful to fully mine these information during modeling for improving the performance of anomaly detection. Meanwhile, there are some redundant information or noise in the log sequence, which has no contribution to the detection, and may even bring negative impact. The existing methods for log sequence anomaly detection do not take the above problems into account when constructing models. In this paper, we propose LSADNET, an unsupervised log sequence anomaly detection network based on local information extraction and globally sparse Transformer model. LSADNET applies multi-layer convolution to capture the local correlation between adjacent logs, and utilizes Transformer to learn the global dependency among long-distance logs. Meanwhile, we propose a globally sparse Transformer model to improve the self-attention mechanism, which can help to retain important information adaptively and eliminate the irrelevant information in the log sequence. In addition, according to the co-occurrence mode of log templates, we put forward the calculation formula of log template transfer value, and apply it to log vectorization. Through sufficient experiments on two public datasets, it is confirmed that LSADNET has better performance than the state-of-art methods.

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences ( g -sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation ( s -sequences) as high-utility hierarchical sequential patterns from g -sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.

The Eclat algorithm is one of the most widely used frequent itemset mining methods. In the normal Eclat algorithm and its variants, it is inefficient to calculate the intersection size of itemsets by sequentially comparing elements, especially for large scale transactions. In this paper, we propose the fast Eclat algorithms that can quickly calculate the intersection size of multiple itemsets by using minwise hashing and the estimators. Minwise hashing is used to calculate the Jaccard similarity coefficient by mapping the elements of the sets to those of smaller sets. Two estimators are used to estimate the intersection size of itemsets based on the Jaccard similarity coefficient. Due to the “imperfect” hash function, minwise hashing may obtain a biased Jaccard similarity, which results in error between the real value and the estimated value of the intersection size. Thus, we proposed the HashEclat which uses the maximum of |A| and |B| to represent the union size |A U B|, and proposed the Sim-Eclat which uses the minimum of |A| and |B| to represent the intersection size |A fl B|. Furthermore, we use a boundary error E for better performance as follows: if E is large, the intersection size is determined by a traditional method, and the result is more accurate but takes longer to compute; otherwise, it will be the opposite. Both the theoretical analysis and experimental results show that the proposed algorithms can obtain almost all frequent itemsets with higher speed and less memory usage than other algorithms.

Radio Frequency Identification (RFID) technology is broadly used in many applications just like Supply Chain and Retail trade. However, owing to the working mechanism of RFID reader and RFID tag, raw RFID readings are not always reliable. False negative induced by low identifying rate in the reader's minor detect region and duplication owing to detection region's spatial overlap are the two main problems that cause unexpected consequence in upper applications of RFID Middleware. In this paper we discriminate the application scenarios by defining the concepts FN-S and “FN-NS” and propose an improved adapted smoothing method and an Bayesian method towards the two different scenarios. Both theoretically and by convincible experiment, we show the superiority of our algorithms.

In this paper, we propose a robust approach to access point (AP) selection problem for the indoor location tracking. It takes the environments changes into account and makes use of residuals ranking algorithm to select those APs least sensitive to the environment changes in indoor location tracking, we call it ResidualRanking method, also we make an improvement of residual computing according to the properties of radio signals. Additionally, we present a location tracking system called BRR (Bayesian and Residuals Ranking) which is based on the Bayesian decision method and the ResidualRanking method we proposed. Finally, we make a comparison to the MaxMean AP selection method, and the experimental results indicate that the ResidualRanking method we proposed can achieve a better performance than the MaxMean method, also the proposed system BBR can get desirable results in the realist indoor location tracking.

Continuous stirred tank reactor system (CSTR) is a typical chemical reactor system with a complex nonlinear dynamic characteristics. In this paper, a recurrent neural network (RNN) evolved by a cooperative scheme is proposed to estimate the state of the CSTR system, which combines the architectural evolution with weight learning. In this scheme, particle swarm optimization (PSO) adoptively constructs the network architectures, then evolutionary algorithm (EA) is employed to evolve the network nodes with this architecture, and this process is automatically alternated. It can effectively alleviate the noisy fitness evaluation problem and the moving target problem. In addition of these, a closer behavioral link between the parents and their offspring is maintained, which improves the efficiency of evolving RNN. The results show that the proposed scheme is able to evolve both the architecture and weights of RNN, and the effectiveness and efficiency is better than the algorithms of TDRB, GA, PSO, and HGAPSO applied to the fully connected RNN.

Using particle swarm optimization (PSO) algorithm to evolve an optimum input subset for a SVM is proposed Binary PSO algorithm is employed in feature selection, in which each particle represented as a binary vector corresponds to a candidate input subset. A swarm of particles flies through the input set space for targeting the optimal subset. In order to evaluate the reasonable fitness of each input subset, PSO algorithm is used to adoptively evolve SVM to obtain the best performance of network, in which each particle represented as a real vector corresponds to the candidate kernel parameters of SVM. This method has been applied in a real financial time series forecasting, the results show that it has better performance of generalization, and higher rate of convergence.

A novel real-coded GA is proposed which utilizes chaotic search and variable evolutionary rate. By introducing these techniques, the GA algorithm can better simulate the process of biologic evolution, and possesses better hill-climbing ability. In addition, "family competition" is added into the process of mutation and the operating order of mutation and crossover operators is adaptively changed in a different evolutionary stage. Compared with self-adaptive GAs, the real-coded GA can overcome the shortcoming of premature convergence and stagnation, and effectively solves the problem of global convergence.

The information processing capability of artificial neural networks (ANNs) is closely related to its architecture and weights. The paper describes a new evolutionary system for evolving artificial feedforward neural networks, which is based on the particle swarm optimisation (PSO) algorithm. Both the architecture and the weights of ANNs are adaptively adjusted according to the quality of the neural network. This process is repeated until the best ANN is accepted or the maximum number of generations has been reached. A strategy of evolving added nodes and a partial training algorithm are used to maintain a close behavioural link between the parents and their offspring. This system has been tested on two real problems in the medical domain. The results show that ANNs evolved by PSONN have good accuracy and generalisation ability.